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It is a common problem that the glass panel falls due to the damage of silicone structural sealant used for the connection of the hidden frame glass curtain walls in the service life of the curtain walls. Most of the existing studies used dynamics methods to detect the debonding damage of curtain walls. However, these methods usually require additional load excitation, and the measured dynamic response data are susceptible to environmental noise, which makes it difficult to achieve reliable assessment of the safety condition of curtain walls. In order to solve the above problems, this paper proposes a method to detect the debonding damage of curtain wall units by using the deflection and rotation angles of the glass panel under the action of negative wind pressure. First, a series of debonding damage experiments of hidden frame glass curtain wall are designed to verify the feasibility of using deflection and rotation angle to detect the safety state of the curtain wall under wind load. Then, based on the experimental results, a safety risk monitoring system for hidden frame glass curtain walls is proposed, which can realize the real-time monitoring of curtain wall units and conduct rapid ide.pngication of debonding damage.

Aiming at the issue of large CPU costs and low calculation accuracy in the design of right-angle fasteners in scaffolding structures, prediction models of fastener anti-slip performance and torsion performance based on machine learning are proposed. A three-dimensional solid model of right-angle fasteners is established using the finite element method, the effectiveness of the numerical simulation method is verified through test results, and the comprehensive influence of various design parameters on the performance of fasteners is revealed by the parametric analysis method. The database is established by combining the test and numerical simulation results, and the fastener stiffness prediction models are proposed based on random forest (RF), support vector machine (SVM) and K-most proximity algorithm (K-NN), respectively. The expressions for the measured point displacement of the anti-slip model and the stiffness prediction of the torsion model are proposed in combination with genetic expression programming. The results indicate that SVM and GEP can predict the displacement and torsional stiffness of right-angle fasteners more accurately, which is important for guiding the safety design of fasteners in engineering scaffolding structures.